UM
Error analysis for the semi-supervised algorithm under maximum correntropy criterion
Zuo, Ling; Wang, Yulong
2017-02-05
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Volume223Pages:45-53
AbstractAs a similarity measure, correntropy has been increasingly employed in machine learning research. While numerous experimental results have shown the effectiveness of correntropy based methods, the theoretical analysis in this area is still poorly understood. In this paper, we propose a novel semi-supervised algorithm under the maximum correntropy criterion, and present an elaborate error analysis for it. An excess generalization error bound is established, which demonstrates that the proposed method is consistent, and converges at a faster rate compared with the related studies. Moreover, experiments are implemented to show the efficiency of the proposed method.
KeywordSemi-supervised learning Correntropy Excess generalization error Manifold error
DOI10.1016/j.neucom.2016.10.023
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000390082100005
PublisherELSEVIER SCIENCE BV
The Source to ArticleWOS
Fulltext Access
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Document TypeJournal article
CollectionUniversity of Macau
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GB/T 7714
Zuo, Ling,Wang, Yulong. Error analysis for the semi-supervised algorithm under maximum correntropy criterion[J]. NEUROCOMPUTING,2017,223:45-53.
APA Zuo, Ling,&Wang, Yulong.(2017).Error analysis for the semi-supervised algorithm under maximum correntropy criterion.NEUROCOMPUTING,223,45-53.
MLA Zuo, Ling,et al."Error analysis for the semi-supervised algorithm under maximum correntropy criterion".NEUROCOMPUTING 223(2017):45-53.
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